The association between ambient pollutants and influenza transmissibility: A nationwide study involving 30 provinces in China

Abstract Background The impact of exposure to ambient pollutants on influenza transmissibility is poorly understood. We aim to examine the associations of six ambient pollutants with influenza transmissibility in China and assess the effect of the depletion of susceptibles. Methods Provincial‐level surveillance data on weekly influenza‐like illness (ILI) incidence and viral activity were utilized to estimate the instantaneous reproduction number (Rt) using spline functions. Log‐linear regression and the distributed lag non‐linear model (DLNM) were employed to investigate the effects of ambient pollutants—ozone (O3), particulate matter ≤2.5 μm (PM2.5), particulate matter ≤10 μm (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)—on influenza transmissibility across 30 Chinese provinces from 2014 to 2019. Additionally, the potential effects of the depletion of susceptibles and regional characteristics were explored. Results There is a significantly positive correlation between influenza transmissibility and five distinct ambient pollutants: PM2.5, PM10, SO2, CO, and NO2. On average, these ambient pollutants explained percentages of the variance in Rt: 0.8%, 0.8%, 1.9%, 1.3%, and 1.4%, respectively. Conversely, O3 was found to be negatively associated with Rt, explaining 1.5% of the variance in Rt. When controlling for the effect of susceptibles depletion, the effects of all pollutants were more pronounced. The effects of PM2.5, PM10, CO, and SO2 were higher in the eastern and southern regions. Conclusions Most ambient pollutants may potentially contribute to the facilitation of human‐to‐human influenza virus transmission in China. This observed association was maintained even after adjusting for variation in the susceptible population.


| INTRODUCTION
Seasonal influenza is an acute respiratory infection caused by influenza viruses and has been a serious public health problem. It is estimated that there are approximately 1 billion cases of influenza worldwide each year, of which 3-5 million are severe, and 290,000-650,000 influenza-associated respiratory deaths (case fatality rate 0.1%-0.2%). 1,2 Influenza can be transmitted by droplets made during infections with cough, sneeze, or talk. The amount of host-virus excretion, population susceptibility, and viability of the influenza virus in the environment are three key factors that affect influenza transmissibility.
Several studies have reported that human mobility, 3 climate, 4,5 nonpharmaceutical interventions, 6 ambient pollutants, 7 and the types of the virus 8 affected the host-to-host transmission of influenza by acting on one or more of the above aspects.
Ambient air pollutants were considered potential drivers of influenza activity. [9][10][11] For instance, Wang et al. demonstrated that particulate matter ≤2.5 μm (PM 2.5 ) was significantly associated with an increased risk of pediatric seasonal influenza cases in Shijiazhuang. 10 Yang and colleagues found that most air pollutants (including PM 2.5 , particulate matter ≤10 μm [PM 10 ], nitrogen dioxide [NO 2 ], sulfur dioxide [SO 2 ], and carbon monoxide [CO]) were associated with an increased risk of influenza-like illness (ILI) in 30 provinces in China. 9 PM 10 and ozone (O 3 ) were associated with more pediatric influenza cases in Brisbane. 7 However, little is known about the impact of exposure to ambient air pollutants on influenza transmissibility.
The dependent variable of most studies on the association between ambient pollutants and influenza is the absolute count of ILI. 11,12 Because of the differences in the distribution of sentinel hospitals and population size and density within cities, the number of ILI cases is not an ideal proxy for evaluating influenza transmissibility. 13 Minor changes in influenza transmissibility could have a substantial effect on its incidence by dynamical resonance. 14 For this reason, the daily effective instantaneous reproduction number (R t ), defined as the average number of secondary infections caused by a typical single infectious individual at time t, is considered a more rational proxy for exploring the impact of environmental factors on influenza transmissibility than absolute ILI cases. 13,15 In addition, in previous studies, researchers considered the modification or confounding effects of environmental factors, such as temperature, humidity, and population size, on the association between ambient pollutants and influenza. 11,12,15 But they did not pay enough attention to the impact of the depletion of susceptibles in the population with the development of the epidemic. The proportion of susceptible individuals in a population is a key determinant of the spread and size of an infectious disease epidemic 16 and thus may directly or indirectly affect the association between environmental factors and influenza.
Therefore, the goals of our study were to examine the associations of six ambient pollutants (O 3 , PM 2.5 , PM 10 , NO 2 , SO 2 , and CO) with influenza transmissibility (R t ) in 30 provinces of China and assess the effect of the depletion of susceptibles. Daily concentrations of six ambient pollutants in 30 provinces were obtained from the China High Air Pollutants (CHAP) dataset. 17 Hourly ambient temperature and dew point temperature data were obtained from the China Meteorological Administration to calculate relative humidity and absolute humidity using the R package "humidity" (R software, version 4.2.1). Weekly ILI and viral detection rate data were obtained from the Chinese National Influenza Surveillance Network. Based on previous studies, 18,19 proxy measures of the weekly incidence rate were obtained by multiplying the ILI percentage among patients visiting sentinel hospitals with the proportion of influenzapositive specimens. This proxy is considered a precise representation of the activity of an influenza infection. 20,21 We multiplied the weekly incidence rate by a constant (10,000), representing the inverse of the coverage of the sentinel sites in the studied provinces, and rounded the resulting values to the nearest integers to obtain a time series of weekly incidence rate counts. 22 Influenza epidemics were defined as outbreaks exceeding the epidemic threshold for at least seven consecutive weeks or more. The epidemic threshold was determined as the 50th percentile of all the non-zero weekly incidence rate counts over the study period. 21,22 Spline functions were used to interpolate the weekly incidence rate counts to produce daily incidence rate counts, which were used to estimate transmissibility. 21,22 2.2 | R t and adjusted R t estimation Daily R t , a real-time measure of transmissibility, is estimated according to the Bayesian framework applied to the branching process model proposed by Cori et al., 23 which is an extension of the Fraser method. 24 We assumed a gamma distribution with a mean of 2.6 days and a standard deviation of 1.5 days as the serial interval distribution. 25 As an epidemic progresses, the number of susceptible individuals in the population will decrease, and R t can gradually decrease as a result. Therefore, to adjust for this effect, we calculated the adjusted R t according to Ali et al. 22 The detailed estimation process of R t and adjusted R t can be found in Supporting information.

| Statistical analyses
Our current statistical analysis strategy is composed of two parts: univariate analysis and multivariate analysis. First, we employed a loglinear regression with a 0-7 day lag to identify significant environmental drivers of R t /adjusted R t . To ascertain whether the association between each environmental variable and R t /adjusted R t occurred by chance, we performed a permutation test on these log regression models. This was done using 1000 dummy or null scenarios, and the results were compared with a real-time series. Only when the p-value from the permutation test was less than 0.05 was the corresponding environmental variable considered a significant driver of R t /adjusted R t . It was subsequently included in the multivariate analysis.
Secondly, we used distributed lag non-linear models with a lag of 0-7 days (DLNM, R software, version 2.4.7) to quantify the impact of individual drivers. This was achieved by comparing R-square (R 2 ) values for Model 1 and Model 2. Model 1 evaluated the impacts of depletion in susceptibility over time and/or inter-epidemic effects, temperature, and absolute humidity on Rt/adjusted Rt. Model 2 incorporated the additional effect of the respective ambient pollutant.
To examine whether the impacts of ambient pollutants varied by region, we classified the 30 provinces into seven different regions ( Figure 1), following the categorization used in a previous study. 9 We then estimated the region-specific associations between ambient pollutants and R t by fitting models for each category.

| Background characteristics by provinces
The median of the maximum R t for all epidemics across the 30 provinces is 1.57, ranging from 1.36 to 2.04. Table 1 presents summary statistics of ambient pollutants, air temperature, and relative and absolute humidity in 30 Chinese provinces from 2014 to 2019.
Generally, the O 3 concentration was higher in the eastern and southern regions, while the concentrations of PM 2.5 , PM 10 , NO 2 , SO 2 , and CO were higher in the northern and northeast regions. The weather in the high-latitude regions, such as the northern, northeast, and northwest areas, tended to be colder and drier.

| Univariate regression model
We explored the association between influenza transmissibility, as measured by R t /adjusted R t and each factor with lagged values of 0-7 days for each province. Except for O 3 , other pollutants (including PM 2.5 , PM 10 , SO 2 , NO 2 , and CO) have a positive correlation with R t / adjusted R t (data did not show). Tables S1 and S2 show the results from the non-linear regression model and permutation test. Air temperature, relative humidity, absolute humidity, and six ambient pollutants were significantly correlated with R t /adjusted R t in almost all provinces, although the variance of the R t /adjusted R t explained by each driver was marginal.
Permutation analysis indicated that a substantially lesser variance in transmissibility was accounted for by the 1000 null/dummy time series for each respective driver when compared with the observed true time series. The difference in variance between the permutation analysis and the observed true time series was found to be statistically significant in most locations. These significant drivers were included in further analysis.

| Multivariable regression analysis
In stratified multivariate regression analysis of R t by location, Model 2-which incorporates all influencers such as depletion of susceptibles F I G U R E 1 The 30 Chinese provinces were divided into seven regions. The Tibet autonomous region was excluded from our analysis due to its high proportion of missing data on influenzalike illness.
T A B L E 1 Summary of the instantaneous reproductive number (R t ), ambient pollutants, and meteorological variables, presented as median and interquartile range (IQR), across 30 Chinese provinces from 2014 to 2019.   Figure 3. In scenarios where the effect of depletion of susceptibles was not considered, the impacts of pollutants on R t did not exhibit substantial regional disparities, with the exception of CO. By factoring in the effect of the depletion of susceptibles, the proportion of R t attributable to all pollutants increased.

| Estimates of the variance of R t /adjusted R t variation across regions
In particular, the contribution of O 3 to the adjusted R t in the southern region was weaker than in other regions. T A B L E 2 Percentage of the variance of the instantaneous reproduction number (R t ) explained by the ambient pollutants, from models on pre-defined influenza epidemics in respective locations from 2014 to 2019. The results based on the distributed lag model (DLNM) with lags of 0-7 days.

Province Models
With unadjusted R t R 2 or R 2 %ΔR 2 R 2 O 3 PM 2.5 PM 10 SO 2 CO NO 2 All   obstructive pulmonary disease, and asthma. 26 Moreover, a consistent body of epidemiological studies has emphatically demonstrated a strong correlation between air pollution and the increased risk of influenza. 7,9,11 Our results, stemming from an analysis of five ambient pollutants and their relation to influenza transmissibility, align with prior studies' conclusions: exposure to most ambient pollutants is commonly linked to adverse health outcomes, whether they be chronic or infectious diseases. Interestingly, our study reveals a  The mechanisms underlying the association between air pollution and influenza transmissibility are less elucidated. One potential explanation for our findings might be that exposure to air pollution induces oxidative stress, impairs the activation of macrophage-dependent invasive pathogens, and exacerbates inflammation. 28  O 3 can inactivate the influenza virus within hours, 34 and animal toxicology studies have shown a decrease in inflammation, injury, and oxidative stress following exposure to O 3 . 35 China, as a vast country with diverse regions, might observe varying impacts of ambient pollutants on influenza transmissibility due to regional differences. Our study has discovered that the influence of ambient pollutants on transmissibility is stronger in the eastern and southern regions. Our findings align with a recent epidemiological study that reported that the effects of air pollutants on ILI in the eastern and central regions of China were higher than those in other regions. 9 The exact underlying mechanisms of the heightened effects of ambient pollutants on transmissibility remain unclear and require further research. The stronger effect of ambient pollution in the eastern and southern regions may be partly attributable to personal exposure patterns. For instance, these regions generally offer more pleasant climates, leading to individuals spending more time outdoors and exhibiting a greater inclination toward using natural ventilation in buildings. 36,37 Such behaviors could potentially enhance the effects of ambient air pollution.

Northern
One limitation of the current study was that the seasonal influenza data were collected from surveillance sentinel hospitals, and values varied between years, which could have negatively affected the results. In addition, we interpolated daily incidence rates from the weekly data, which may artificially reduce variability and lead to underestimated effects. Thus, where available, using daily positive ILI rate data would likely prove advantageous. Finally, some climate factors, such as solar radiation and absolute humidity, were not included in the time-series models due to limited data. These factors may have an impact on the concentration of ambient pollutants and the R t .
In conclusion, by using influenza transmissibility rather than reported ILI cases or incidence rates, we found that most ambient pollutants (i.e., PM 2.5 , PM 10 , NO 2 , SO 2 , and CO) were significantly associated with increased influenza transmissibility. The association between ambient pollutants and influenza transmissibility remained significant after adjusting the effect of depletion in susceptibles.

ACKNOWLEDGMENTS
For the purpose of open access, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission.

CONFLICT OF INTEREST STATEMENT
The authors declare that they have no competing interests.

DATA AVAILABILITY STATEMENT
Because of the potentially sensitive information included, the original dataset is not public and is available from the corresponding author upon reasonable request.

ETHICS APPROVAL STATEMENT
As this study is based on a secondary dataset without any identifying individual information, ethical approval was not needed. This study used a secondary dataset, and thus patient consents are not applicable.